Quantification of nucleic acids is important in a number of fields ranging from molecular biology and genetic research to diagnostics and pathogen detection. When sufficient amounts of nucleic acids are present blot-techniques can be applied for quantification. However, the limited sensitivity of these techniques prevents their use in a number of cases.
Quantitative PCR-methods developed in the recent past provide tools for analysis in cases where much higher sensitivity is required. These techniques are based on the fact that during PCR amplification the amount of product grows exponentially and, thus, the amount of product obtained after a comparably small number of cycles can be detected by conventional means (e.g. fluorescence detection). Further, in principle the amount of product that was present initially, i.e. at the beginning of the amplification, can be determined from the amount of product obtained at the end of the amplification if the number of amplification cycles is known.
Briefly outlined, in practice, target nucleic acids as well as standard and/or comparative samples are subjected to PCR under defined reaction conditions and formation of PCR product is monitored over the course of the amplification process. Detection of PCR product is achieved e.g. by means of fluorescently labeled hybridization probes emitting specific signals when bound to the target or by means of DNA-intercalating fluorescence dyes that allow to detect double strand product. The number of amplification cycles that are necessary to obtain a particular fluorescence threshold-level, designated as Ct-values, are determined and the Ct-value of the target is compared to the Ct-values of the samples of a dilution series of a nucleic acid standard with known concentration (absolute quantification). In order to determine the absolute quantity of the target a standard curve is constructed from the Ct-values of the standard samples and used to determine the initial concentration of the target. Alternatively, the Ct-value of the target is compared to the Ct-value of a single comparative nucleic acid of interest (relative quantification). In this case the ratio of the Ct-values of target and comparative sample is determined and used to assess the ratio of the initial quantities of target and comparative nucleic acid. Unfortunately, the practical application of these approaches is complicated by a number of problems, some of which will be discussed below.
Some challenges in the field of quantitative PCR are related to the growing need to analyze large numbers of samples in short intervals of time. As an ever increasing range of applications for quantitative PCR requires analysis of very large numbers of samples in a high-throughput fashion, e.g. in clinical practice, it is necessary to develop quantitative PCR-methods that can be automated completely and require very little or no human interaction. This is of crucial importance in some cases as high throughput applications (e.g. in clinical practice) simply cannot be conducted in the required short periods of time if human interaction is required.
An additional benefit that could be realized with such automated methods would be an improved comparability of analytical data between different labs currently employing widely differing laboratory protocols for quantitative PCR. The issue is of paramount importance in view of an increasing number of labs using quantitative PCR-techniques for basic research. Establishing an automated method as an objective reference for quantification experiments would drastically benefit these research efforts.
A typical plot of PCR product formed over the course of an amplification reaction reveals four different phases of the amplification process (see FIG. 1): (1) The ground phase where the fluorescence signal is dominated by background fluorescence and noise; (2) an exponential phase where the signal from PCR product rises above ground level and increases exponentially; (3) a log-linear phase where the signal increases with less than exponential rate due to decreasing amplification efficiency caused by factors as the consumption of PCR reagents and degradation of detection probes; (4) a plateau phase with marginal rise of the signal due to an increasing slowdown and ultimately stop of the amplification reaction.
Despite the seemingly straightforward concept, choosing a suitable signal-threshold level in order to determine Ct-values is not a simple task. As can be seen from the plot of FIG. 1 choosing a high threshold level can lead to Ct-values in the log-linear or plateau-phase of the amplification reaction. This is undesirable as the reaction has slowed down from exponential growth in this region obscuring the correlation between initial and present amounts of nucleic acids. Choosing a low threshold-level, on the other hand, can result in a Ct-value located in the ground phase of the amplification reaction where noise and background signal may complicate measurements. Clearly, thus, it is desirable to pick a threshold level resulting in Ct-values corresponding to cycles with exponential growth, i.e. in the exponential phase.
A number of approaches have been developed to achieve this goal (see e.g. J D Durtschi et al. Analytical biochemistry 2007 (361) 55-64). The basic threshold approach requires an experimenter to look at the amplification curve and use his own judgment to pick a suitable signal threshold that is crossed in the exponential phase. Since the absolute level of the signal depends on a number of factors including length of the nucleic acid and means of detection used the threshold level for this method has to be checked and, if necessary, adjusted for each application. Similarly, in the fit-point method an experimenter has to pick a suitable threshold level by his own judgment. However, instead of assigning Ct to the cycle number where the recorded signal crosses the threshold, a linear fit is modeled on a semi-logarithmic plot of the exponential part of the curve and the cycle where this straight line passes the threshold is designated as Ct. Another concept is called the second derivative maximum method where the maximum of the second derivative of the amplification curve is determined numerically. The corresponding cycle is assumed to represent the end of the exponential growth phase, where the reaction begins to slow down to linear growth. This cycle number is used, analogously to Ct, for determining the quantity of the target. In contrast to the basic threshold and the fit-point method this method requires minimal or no human interaction as no threshold level has to be set by an experimenter. Yet another approach is called the sigmoidal curve-fitting method where a sigmoid function is modeled upon the amplification curve. The cycle number corresponding to the inflection point of the curve can be obtained from the model and is used, analogously to Ct, for determining the quantity of the target. This method, as well, requires minimal or no human interaction. The second derivative maximum method and the sigmoidal curve-fitting method, in principle, appear suitable for complete automation. The basic threshold method and the fit-point method, by nature however, require human interaction and, thus, are not suitable for automation. The second derivative maximum method and the sigmoidal curve-fitting method, however, have been found to be of limited use for applications requiring high sensitivity (J D Durtschi et al. Analytical biochemistry 2007 (361) 55-64).
It is, therefore, an object of the present invention to provide methods that are suitable for fully automated quantification of nucleic acids with high sensitivity.